Survdat Abundance and Biomass Check
Adam A. Kemberling
2021-03-16
Checking Species Abundance and Biomass for Previous Works
While investigating some discrepancies among different datasets a handful of corrections were made. This document is intended to see if any of those changes have important implications for some of the species-specific reports that have been done recently.
The three datasets being compared here are the survdat_Nye_allseasons.RData, Survdat_Nye_Aug_2020.RData, and the more recent NEFSC_BTS_all_seasons_03032021.RData.
Load SURVDAT Sources
# Cleanup functions
source(here("R/01_nefsc_ss_build.R"))
#### Survdat resource Load Data
# 2019 Data used in 2020 paper
load(paste0(nmfs_path, "Survdat_Nye_allseason.RData"))
survdat_nye <- survdat %>% clean_names()
# Run cleanup
survdat_nye <- survdat_prep(survdat = survdat_nye) %>%
mutate(survdat_source = "survdat_nye")
# 2020 data received last august
load(paste0(res_path, "NMFS_trawl/Survdat_Nye_Aug 2020.RData"))
survdat_20 <- clean_names(survdat)
# Run cleanup
survdat_20 <- survdat_prep(survdat = survdat_20) %>%
mutate(survdat_source = "survdat_nye2020")
# Data we just received in 2021 with errors located and corrected
load(paste0(nmfs_path, "NEFSC_BTS_all_seasons_03032021.RData"))
survdat_21 <- survey$survdat %>% clean_names()
# Run cleanup
survdat_21 <- survdat_prep(survdat = survdat_21) %>%
mutate(survdat_source = "survdat_2021")
rm(survdat, survey)Load List of Published Species
#### Load the species list from Andrew
species_check <- read_csv(here("data/andrew_species/Assesmentfishspecies.csv"),
col_types = cols())
species_check <- species_check %>%
clean_names() %>%
mutate(svspp = str_pad(svspp, 3, pad = "0", side = "left"),
comname = tolower(comname),
species = str_to_title(species)) %>%
arrange(svspp)
# Put in a list
source_list <- list("survdat_nye_2019" = survdat_nye,
"survdat_nye_2020" = survdat_20,
"survdat_2021" = survdat_21)Making Comparisons
In this code chunk the different sources are put together in a list, and the same cleanup steps are applied to each. The years are limited to before 2017 so there are no extra years in some not available in others. The SVSPP codes are also limited to just the ones in the list Andrew provided.
After that repeated records for abundance and biomass are dropped. These repeats exist to accommodate the length and number at length details. We don’t need them for this so they are dropped here.
# Make years comparable
# Filter species down for both
source_list <- map(source_list, function(survdat_data){
# filter years
sdat <- survdat_data %>%
filter(est_year >= 2008,
est_year <= 2017,
svspp %in% species_check$svspp)
# Pull distinct station abundance and biomass for each species
sdat <- sdat %>%
distinct(id, est_year, season, svvessel,
comname, svspp, catchsex, abundance, biom_adj)
})
# Split each survdat set into a list of single species tables
source_splits <- map(source_list, function(source_data){
source_split <- source_data %>% split(.$comname) })To make comparisons between the different data sets I first check that the svspp code is present in the dataset. Then for each source I get a total annual value for biomass and abundance. Then across data sources I calculate the correlation between these values, and the percent change from one data source to the most recently revised data pull.
The table itself is retained in addition to some overall summary metrics.
# Make Comparisons
# Vector of species and their common names, atleast the common names Andrew used
andrew_species <- species_check$comname %>% setNames(species_check$svspp)
# Pulling out details for the species
species_comparisons <- imap(andrew_species, function(species_comname, species_svspp){
# there are some name mismatches in common name, so catch those and return an error
# that explains which data it was not in
in_nye <- species_comname %in% names(source_splits$survdat_nye_2019)
in_20 <- species_comname %in% names(source_splits$survdat_nye_2020)
in_21 <- species_comname %in% names(source_splits$survdat_2021)
in_both <- in_nye & in_21
in_all <- in_nye & in_20 & in_21
if(in_both == FALSE){
return(list("in_nye" = in_nye,
"in_21" = in_21,
"in_all" = in_all,
"data" = data.frame(),
"metrics" = data.frame())) }
# Pull just that species from the 2019 data
summ_19 <- source_list$survdat_nye_2019 %>%
filter(svspp == species_svspp) %>%
group_by(comname, est_year) %>%
summarise(abund_19 = sum(abundance, na.rm = T),
biom_19 = sum(biom_adj, na.rm = T),
.groups = "keep") %>% ungroup()
# same for 2020 data
summ_20 <- source_list$survdat_nye_2020 %>%
filter(svspp == species_svspp) %>%
group_by(comname, est_year) %>%
summarise(abund_20 = sum(abundance, na.rm = T),
biom_20 = sum(biom_adj, na.rm = T),
.groups = "keep") %>% ungroup()
# and 2021 data
summ_21 <- source_list$survdat_2021 %>%
filter(svspp == species_svspp) %>%
group_by(comname, est_year) %>%
summarise(abund_21 = sum(abundance, na.rm = T),
biom_21 = sum(biom_adj, na.rm = T),
.groups = "keep") %>% ungroup()
# join them for side-by-side data
combined_data <- left_join(summ_19, summ_20, by = c("comname", "est_year")) %>%
left_join(summ_21, by = c("comname", "est_year")) %>%
mutate(
# 2019-2021 comparison
abund_change_19to21 = ((abund_21 - abund_19) / abund_19) * 100,
biom_change_19to21 = ((biom_21 - biom_19) / biom_19) * 100,
# 2020-2021 comparison
abund_change_20to21 = ((abund_21 - abund_20) / abund_20) * 100,
biom_change_20to21 = ((biom_21 - biom_20) / biom_20) * 100)
#return(combined_data)
# abundance correlation
abund_cor_19to21 <- cor(combined_data$abund_21,
combined_data$abund_19,
use = "pairwise.complete.obs")
abund_cor_20to21 <- cor(combined_data$abund_21,
combined_data$abund_20,
use = "pairwise.complete.obs")
# biomass correlation
biomass_cor_19to21 <- cor(combined_data$biom_21,
combined_data$biom_19,
use = "pairwise.complete.obs")
biomass_cor_20to21 <- cor(combined_data$biom_21,
combined_data$biom_20,
use = "pairwise.complete.obs")
# average relative shift
abund_shift_19to21 <- mean(combined_data$abund_change_19to21, na.rm = T)
biom_shift_19to21 <- mean(combined_data$biom_change_19to21, na.rm = T)
abund_shift_20to21 <- mean(combined_data$abund_change_20to21, na.rm = T)
biom_shift_20to21 <- mean(combined_data$biom_change_20to21, na.rm = T)
# put in list to export
list("data" = combined_data,
"metrics" = data.frame(
"comname" = species_comname,
"svspp" = species_svspp,
"abund_corr_19to21" = abund_cor_19to21,
"abund_corr_20to21" = abund_cor_20to21,
"biom_corr_19to21" = biomass_cor_19to21,
"biom_corr_20to21" = biomass_cor_20to21,
"perc_abund_19to21" = abund_shift_19to21,
"perc_abund_20to21" = abund_shift_20to21,
"perc_biom_19to21" = biom_shift_19to21,
"perc_biom_20to21" = biom_shift_20to21)
)
})For simplicity I then append the tables together so they are single data frames and not lists.
# put data and metrics into a table
comparison_data <- map_dfr(species_comparisons, ~.x[["data"]])
comparison_metrics <- map_dfr(species_comparisons, ~.x[["metrics"]]) Visualizing the three data sources Abundance/Biomass
As a quick visual reference, here is how total annual abundance and biomasses compare across data sources. The species have been split into groups to make paneling less crowded.
# How many species are there, and how many can we panel well - 84
#unique(andrew_species)
# make even number groups
even_groups <- list(
group_1 = sort(andrew_species)[1:17],
group_2 = sort(andrew_species)[18:34],
group_3 = sort(andrew_species)[35:51],
group_4 = sort(andrew_species)[52:68],
group_5 = sort(andrew_species)[69:84])Building plots
timeseries_plots <- map(even_groups, function(species_subset){
species_subset_dat <- comparison_data %>%
filter(comname %in% species_subset) %>%
mutate(comname = str_to_title(comname),
comname = fct_drop(comname),
comname = fct_inorder(comname))
# abundance plot
abundance_plot <- ggplot(species_subset_dat, aes(est_year)) +
geom_line(aes(y = abund_19,
color = "Survdat Nye Allseason 2019",
linetype = "Survdat Nye Allseason 2019"), group = 1) +
geom_line(aes(y = abund_20,
color = "Survdat Nye - August 2020",
linetype = "Survdat Nye - August 2020"), group = 1) +
geom_line(aes(y = abund_21,
color = "Newest Survdat",
linetype = "Newest Survdat"), group = 1) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
facet_wrap(~comname, scales = "free") +
labs(x = "", y = "Annual Abundance",
color = "Survdat Retrieval",
linetype = "Survdat Retrieval") +
theme(legend.position = "none")
biomass_plot <- ggplot(species_subset_dat, aes(est_year)) +
geom_line(aes(y = biom_19,
color = "Survdat Nye Allseason 2019",
linetype = "Survdat Nye Allseason 2019"), group = 1) +
geom_line(aes(y = biom_20,
color = "Survdat Nye - August 2020",
linetype = "Survdat Nye - August 2020"), group = 1) +
geom_line(aes(y = biom_21,
color = "Newest Survdat",
linetype = "Newest Survdat"), group = 1) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
facet_wrap(~comname, scales = "free") +
labs(x = "", y = "Annual Biomass",
color = "Survdat Retrieval",
linetype = "Survdat Retrieval") +
theme(legend.position = "bottom")
return(list(a_plot = abundance_plot, b_plot = biomass_plot,
stacked = (abundance_plot / biomass_plot)))
})Group 1
timeseries_plots$group_1$stacked
Group 2
timeseries_plots$group_2$stacked
Group 3
timeseries_plots$group_3$stacked
Group 4
timeseries_plots$group_4$stacked
Group 5
timeseries_plots$group_5$stacked
Comparing 2019 to 2021
Abundance
Correlation
comparison_metrics %>%
mutate(comname = fct_reorder(comname, abund_corr_19to21, max, .desc = F)) %>%
ggplot(aes(x = abund_corr_19to21, y = comname)) +
geom_segment(aes(xend = 0, yend = comname), color = gmri_cols("gmri blue")) +
geom_point(color = gmri_cols("gmri blue")) +
labs(y = "", x = "Correlation Between Total Annual Abundances")
Annual Percent Change
comparison_metrics %>%
mutate(comname = fct_reorder(comname, perc_abund_19to21, max, .desc = F)) %>%
ggplot(aes(x = perc_abund_19to21, y = comname)) +
geom_segment(aes(xend = 0, yend = comname), color = gmri_cols("gmri blue")) +
geom_point(color = gmri_cols("gmri blue")) +
scale_x_continuous(labels = scales::percent_format()) +
labs(y = "", x = "Avg. Percent Change in Annual Abundance\nMoving from Old Data -> New Data")
Problem Species
Haddock Spotlight
ab_abund <- comparison_data %>%
filter(comname == "haddock") %>%
ggplot(aes(est_year)) +
geom_line(aes(y = abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(y = abund_21, color = "Newest Survdat")) +
scale_color_gmri() +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
labs(x = "", y = "Abundance", subtitle = "Haddock Absolue Abundance", color = "") +
theme(legend.position = "bottom")
p_change <- comparison_data %>%
filter(comname == "haddock") %>%
ggplot(aes(est_year, abund_change_19to21)) +
geom_line() +
scale_x_continuous(breaks = scales::pretty_breaks()) +
labs(y = "Percent Change in Abundance\n2019 -> 2021", x = "")
ab_abund / p_change
Poor Correlation
# correlation
corr_cutoff <- 0.8
corr_species <- comparison_metrics %>%
filter(abund_corr_19to21 <= corr_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
# filter species
corr_sub <- comparison_data %>%
filter(comname %in% corr_species)
# how many species per plot
p1 <- corr_species[1:6]
p2 <- corr_species[-c(1:6)]
# p1
corr_sub %>%
filter(comname %in% p1) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free" ) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Abundance",
title = paste0("Species with Annual Abundance Correlation <= ", corr_cutoff),
color = "Survdat Source",
subtitle = "Subset 1")
# p1
corr_sub %>%
filter(comname %in% p2) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free" ) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Abundance",
title = paste0("Species with Annual Abundance Correlation <= ", corr_cutoff),
color = "Survdat Source",
subtitle = "Subset 2")
Large Percent Change
# percent change
perc_cutoff <- 25
perc_species <- comparison_metrics %>%
filter(perc_abund_19to21 >= perc_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
comparison_data %>%
filter(comname %in% perc_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, abund_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free") +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Abundance",
title = paste0("Species with Avg. Shift in Abundance >= ", perc_cutoff, "%"),
color = "Survdat Source")
Biomass
Correlation
comparison_metrics %>%
mutate(comname = fct_reorder(comname, biom_corr_19to21, max, .desc = F)) %>%
ggplot(aes(x = biom_corr_19to21, y = comname)) +
geom_segment(aes(xend = 0, yend = comname), color = gmri_cols("gmri blue")) +
geom_point(color = gmri_cols("gmri blue")) +
labs(y = "", x = "Correlation Between Total Annual Biomasses")
Annual Percent Change
comparison_metrics %>%
mutate(comname = fct_reorder(comname, perc_biom_19to21, max, .desc = F)) %>%
ggplot(aes(x = perc_biom_19to21, y = comname)) +
geom_segment(aes(xend = 0, yend = comname), color = gmri_cols("gmri blue")) +
geom_point(color = gmri_cols("gmri blue")) +
labs(y = "", x = "Avg. Percent Change in Annual Biomasses\nMoving from Old Data -> New Data")
Problem Species
Haddock Spotlight
ab_biom <- comparison_data %>%
filter(comname == "haddock") %>%
ggplot(aes(est_year)) +
geom_line(aes(y = biom_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(y = biom_21, color = "Newest Survdat")) +
scale_color_gmri() +
scale_y_continuous(labels = scales::comma_format()) +
labs(x = "", y = "Biomass", subtitle = "Haddock Absolue Biomass", color = "") +
theme(legend.position = "bottom")
p_change <- comparison_data %>%
filter(comname == "haddock") %>%
ggplot(aes(est_year, biom_change_19to21)) +
geom_line() +
labs(y = "Percent Change in Biomass\n2019 -> 2021", x = "")
ab_biom / p_change
Poor Correlation
# correlation
corr_cutoff <- 0.8
corr_species <- comparison_metrics %>%
filter(biom_corr_19to21 <= corr_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
# plot
if(length(corr_species) > 0) {
comparison_data %>%
filter(comname %in% corr_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, biom_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, biom_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free" ) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Biomass",
title = paste0("Species with Annual Biomass Correlation <= ", corr_cutoff),
color = "Survdat Source")
}
Large Percent Change
# percent change
perc_cutoff <- 25
perc_species <- comparison_metrics %>%
filter(perc_biom_19to21 >= perc_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
comparison_data %>%
filter(comname %in% perc_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, biom_19, color = "Survdat Nye Allseason 2019")) +
geom_line(aes(est_year, biom_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free") +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Biomass",
title = paste0("Species with Avg. Shift in Biomass >= ", perc_cutoff, "%"),
color = "Survdat Source")
Comparing 2020 to 2021
Abundance
Correlation
comparison_metrics %>%
mutate(comname = fct_reorder(comname, abund_corr_20to21, max, .desc = F)) %>%
ggplot(aes(x = abund_corr_20to21, y = comname)) +
geom_segment(aes(xend = 0, yend = comname), color = gmri_cols("gmri blue")) +
geom_point(color = gmri_cols("gmri blue")) +
labs(y = "", x = "Correlation Between Total Annual Abundances")
Annual Percent Change
comparison_metrics %>%
mutate(comname = fct_reorder(comname, perc_abund_20to21, max, .desc = F)) %>%
ggplot(aes(x = perc_abund_20to21, y = comname)) +
geom_segment(aes(xend = 0, yend = comname), color = gmri_cols("gmri blue")) +
geom_point(color = gmri_cols("gmri blue")) +
scale_x_continuous(labels = scales::percent_format()) +
labs(y = "", x = "Avg. Percent Change in Annual Abundance\nMoving from Old Data -> New Data")
Problem Species
Poor Correlation
# correlation
corr_cutoff <- 0.8
corr_species <- comparison_metrics %>%
filter(abund_corr_20to21 <= corr_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
# filter species
corr_sub <- comparison_data %>%
filter(comname %in% corr_species)
if(length(corr_species) != 0){
# how many species per plot
p1 <- corr_species[1:6]
#p2 <- corr_species[-c(1:6)]
# p1
corr_sub %>%
filter(comname %in% p1) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, abund_20, color = "Survdat Nye - August 2020")) +
geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free" ) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Abundance",
title = paste0("Species with Annual Abundance Correlation <= ", corr_cutoff),
color = "Survdat Source",
subtitle = "Subset 1")
}Large Percent Change
# percent change
perc_cutoff <- 25
perc_species <- comparison_metrics %>%
filter(perc_abund_20to21 >= perc_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
comparison_data %>%
filter(comname %in% perc_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, abund_20, color = "Survdat Nye - August 2020")) +
geom_line(aes(est_year, abund_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free") +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Abundance",
title = paste0("Species with Avg. Shift in Abundance >= ", perc_cutoff, "%"),
color = "Survdat Source")
Biomass
Correlation
comparison_metrics %>%
mutate(comname = fct_reorder(comname, biom_corr_20to21, max, .desc = F)) %>%
ggplot(aes(x = biom_corr_20to21, y = comname)) +
geom_segment(aes(xend = 0, yend = comname), color = gmri_cols("gmri blue")) +
geom_point(color = gmri_cols("gmri blue")) +
labs(y = "", x = "Correlation Between Total Annual Biomasses")
Annual Percent Change
comparison_metrics %>%
mutate(comname = fct_reorder(comname, perc_biom_20to21, max, .desc = F)) %>%
ggplot(aes(x = perc_biom_20to21, y = comname)) +
geom_segment(aes(xend = 0, yend = comname), color = gmri_cols("gmri blue")) +
geom_point(color = gmri_cols("gmri blue")) +
labs(y = "", x = "Avg. Percent Change in Annual Biomasses\nMoving from Old Data -> New Data")
Problem Species
Poor Correlation
# correlation
corr_cutoff <- 0.8
corr_species <- comparison_metrics %>%
filter(biom_corr_20to21 <= corr_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
# plot
if(length(corr_species) > 0){
comparison_data %>%
filter(comname %in% corr_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, biom_20, color = "Survdat Nye - August 2020")) +
geom_line(aes(est_year, biom_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free" ) +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Biomass",
title = paste0("Species with Annual Biomass Correlation <= ", corr_cutoff),
color = "Survdat Source")
}
Large Percent Change
# percent change
perc_cutoff <- 25
perc_species <- comparison_metrics %>%
filter(perc_biom_20to21 >= perc_cutoff) %>%
mutate(comname = fct_drop(comname)) %>%
pull(comname)
comparison_data %>%
filter(comname %in% perc_species) %>%
mutate(comname = fct_drop(comname)) %>%
ggplot() +
geom_line(aes(est_year, biom_20, color = "Survdat Nye - August 2020")) +
geom_line(aes(est_year, biom_21, color = "Newest Survdat")) +
facet_wrap(~comname, ncol = 1, scales = "free") +
scale_y_continuous(labels = scales::comma_format()) +
scale_x_continuous(breaks = scales::pretty_breaks()) +
scale_color_gmri() +
labs(x = "",
y = "Annual Biomass",
title = paste0("Species with Avg. Shift in Biomass >= ", perc_cutoff, "%"),
color = "Survdat Source")
write_csv(comparison_data, here("data/survdat_biom_abund_compare.csv"))A work by Adam A. Kemberling
Akemberling@gmri.org